MultiSlav BiDi Models
Multilingual BiDi MT Models
BiDi is a collection of Encoder-Decoder vanilla transformer models trained on sentence-level Machine Translation task.
Each model is supporting Bi-Directional translation.
BiDi models are part of the
MultiSlav collection. More information will be available soon in our upcoming MultiSlav paper.
Experiments were conducted under research project by
Machine Learning Research lab for
Allegro.com.
Big thanks to
laniqo.com for cooperation in the research.
Graphic above provides an example of an BiDi model -
BiDi-ces-pol to translate from Polish to Czech language.
BiDi-ces-pol is a bi-directional model supporting translation both
form Czech to Polish and
from Polish to Czech directions.
Supported languages
To use a BiDi model, you must provide the target language for translation.
Target language tokens are represented as 3-letter ISO 639-3 language codes embedded in a format >>xxx<<.
All accepted directions and their respective tokens are listed below.
Note that, for each model only two directions are available.
Each of them was added as a special token to Sentence-Piece tokenizer.
| Target Language | First token |
|---|
| Czech | >>ces<< |
| English | >>eng<< |
| Polish | >>pol<< |
| Slovak | >>slk<< |
| Slovene | >>slv<< |
Bi-Di models available
We provided 10 BiDi models, allowing to translate between 20 languages.
Use case quickstart
Example code-snippet to use model. Due to bug the MarianMTModel must be used explicitly.
Remember to adjust source and target languages to your use-case.
1from transformers import AutoTokenizer, MarianMTModel
2
3source_lang = "pol"
4target_lang = "ces"
5first_lang, second_lang = sorted([source_lang, target_lang])
6model_name = f"Allegro/BiDi-{first_lang}-{second_lang}"
7
8tokenizer = AutoTokenizer.from_pretrained(model_name)
9model = MarianMTModel.from_pretrained(model_name)
10
11text = f">>{target_lang}<<" + " " + "Allegro to internetowa platforma e-commerce, na której swoje produkty sprzedają średnie i małe firmy, jak również duże marki."
12
13batch_to_translate = [text]
14translations = model.generate(**tokenizer.batch_encode_plus(batch_to_translate, return_tensors="pt"))
15decoded_translation = tokenizer.batch_decode(translations, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]
16
17print(decoded_translation)
Generated Czech output:
Allegro je online e-commerce platforma, na které své výrobky prodávají střední a malé firmy, stejně jako velké značky.
Training
SentencePiece tokenizer has a vocab size 32k in total (16k per language). Tokenizer was trained on randomly sampled part of the training corpus.
During the training we used the
MarianNMT framework.
Base marian configuration used:
transfromer-big.
All training parameters are listed in table below.
Training hyperparameters:
| Hyperparameter | Value |
|---|
| Total Parameter Size | 209M |
| Vocab Size | 32k |
| Base Parameters | Marian transfromer-big |
| Number of Encoding Layers | 6 |
| Number of Decoding Layers | 6 |
| Model Dimension | 1024 |
| FF Dimension | 4096 |
| Heads | 16 |
| Dropout | 0.1 |
| Batch Size | mini batch fit to VRAM |
| Training Accelerators | 4x A100 40GB |
| Max Length | 100 tokens |
| Optimizer | Adam |
| Warmup steps | 8000 |
| Context | Sentence-level MT |
| Languages Supported | See Bi-Di models available |
| Precision | float16 |
| Validation Freq | 3000 steps |
| Stop Metric | ChrF |
| Stop Criterion | 20 Validation steps |
Training corpora
The main research question was: "How does adding additional, related languages impact the quality of the model?" - we explored it in the Slavic language family.
BiDi models are our baseline before expanding the data-regime by using higher-level multilinguality.
Datasets were downloaded via
MT-Data library.
The number of total examples post filtering and deduplication varies, depending on languages supported, see the table below.
| Language pair | Number of training examples |
|---|
| Czech ↔ Polish | 63M |
| Czech ↔ Slovak | 30M |
| Czech ↔ Slovene | 25M |
| Polish ↔ Slovak | 26M |
| Polish ↔ Slovene | 23M |
| Slovak ↔ Slovene | 18M |
| ---------------- | ------------------------------- |
| Czech ↔ English | 151M |
| English ↔ Polish | 150M |
| English ↔ Slovak | 52M |
| English ↔ Slovene | 40M |
The datasets used (only applicable to specific directions):
| Corpus |
|---|
| paracrawl |
| opensubtitles |
| multiparacrawl |
| dgt |
| elrc |
| xlent |
| wikititles |
| wmt |
| wikimatrix |
| dcep |
| ELRC |
| tildemodel |
| europarl |
| eesc |
| eubookshop |
| emea |
| jrc_acquis |
| ema |
| qed |
| elitr_eca |
| EU-dcep |
| rapid |
| ecb |
| kde4 |
| news_commentary |
| kde |
| bible_uedin |
| europat |
| elra |
| wikipedia |
| wikimedia |
| tatoeba |
| globalvoices |
| euconst |
| ubuntu |
| php |
| ecdc |
| eac |
| eac_reference |
| gnome |
| EU-eac |
| books |
| EU-ecdc |
| newsdev |
| khresmoi_summary |
| czechtourism |
| khresmoi_summary_dev |
| worldbank |
Evaluation
Evaluation of the models was performed on
Flores200 dataset.
The table below compares performance of the open-source models and all applicable models from our collection.
Metric used: Unbabel/wmt22-comet-da.
| Direction | CES → ENG | CES → POL | CES → SLK | CES → SLV | ENG → CES | ENG → POL | ENG → SLK | ENG → SLV | POL → CES | POL → ENG | POL → SLK | POL → SLV | SLK → CES | SLK → ENG | SLK → POL | SLK → SLV | SLV → CES | SLV → ENG | SLV → POL | SLV → SLK |
|---|
| M2M-100 | 87.0 | 89.0 | 92.1 | 89.7 | 88.6 | 86.4 | 88.4 | 87.3 | 89.6 | 84.6 | 89.4 | 88.4 | 92.7 | 86.8 | 89.1 | 89.6 | 90.3 | 86.4 | 88.7 | 90.1 |
| NLLB-200 | 88.1 | 88.9 | 91.2 | 88.6 | 90.4 | 88.5 | 90.1 | 88.8 | 89.4 | 85.8 | 88.9 | 87.7 | 91.8 | 88.2 | 88.9 | 88.8 | 90.0 | 87.5 | 88.6 | 89.4 |
| Seamless-M4T | 87.5 | 80.9 | 90.8 | 82.0 | 90.7 | 88.5 | 90.6 | 89.6 | 79.6 | 85.4 | 80.0 | 76.4 | 91.5 | 87.2 | 81.2 | 82.9 | 80.9 | 87.3 | 76.7 | 81.0 |
| OPUS-MT Sla-Sla | 88.2 | 82.8 | - | 83.4 | 89.1 | 85.6 | - | 84.5 | 82.9 | 82.2 | - | 81.2 | - | - | - | - | 83.5 | 84.1 | 80.8 | - |
| OPUS-MT SK-EN | - | - | - | - | - | - | 89.5 | - | - | - | - | - | - | 88.4 | - | - | - | - | - | - |
| Our contributions: | | | | | | | | | | | | | | | | | | | | |
| BiDi Models* | 87.5 | 89.4 | 92.4 | 89.8 | 87.8 | 86.2 | 87.2 | 86.6 | 90.0 | 85.0 | 89.1 | 88.4 | 92.9 | 87.3 | 88.8 | 89.4 | 90.0 | 86.9 | 88.1 | 89.1 |
| P4-pol◊ | - | 89.6 | 90.8 | 88.7 | - | - | - | - | 90.2 | - | 89.8 | 88.7 | 91.0 | - | 89.3 | 88.4 | 89.3 | - | 88.7 | 88.5 |
| P5-eng◊ | 88.0 | 89.0 | 90.7 | 89.0 | 88.8 | 87.3 | 88.4 | 87.5 | 89.0 | 85.7 | 88.5 | 87.8 | 91.0 | 88.2 | 88.6 | 88.5 | 89.6 | 87.2 | 88.4 | 88.9 |
| P5-ces◊ | 87.9 | 89.6 | 92.5 | 89.9 | 88.4 | 85.0 | 87.9 | 85.9 | 90.3 | 84.5 | 89.5 | 88.0 | 93.0 | 87.8 | 89.4 | 89.8 | 90.3 | 85.7 | 87.9 | 89.8 |
| MultiSlav-4slav | - | 89.7 | 92.5 | 90.0 | - | - | - | - | 90.2 | - | 89.6 | 88.7 | 92.9 | - | 89.4 | 90.1 | 90.6 | - | 88.9 | 90.2 |
| MultiSlav-5lang | 87.8 | 89.8 | 92.5 | 90.1 | 88.9 | 86.9 | 88.0 | 87.3 | 90.4 | 85.4 | 89.8 | 88.9 | 92.9 | 87.8 | 89.6 | 90.2 | 90.6 | 87.0 | 89.2 | 90.2 |
◊ system of 2 models
Many2XXX and
XXX2Many, see
P5-ces2many
* results combined for all bi-directional models; each values for applicable model
Limitations and Biases
We did not evaluate inherent bias contained in training datasets. It is advised to validate bias of our models in perspective domain. This might be especially problematic in translation from English to Slavic languages, which require explicitly indicated gender and might hallucinate based on bias present in training data.
License
The model is licensed under CC BY 4.0, which allows for commercial use.
Citation
TO BE UPDATED SOON 🤗
Contact Options
Authors:
Please don't hesitate to contact authors if you have any questions or suggestions:
